text: import argparse
  from argparse import Namespace
  from typing import Dict
  
  import pytorch_lightning as pl
  
  from baal.active import ActiveLearningDataset
  from baal.utils.ssl_iterator import SemiSupervisedIterator
  
  
  class SSLModule(pl.LightningModule):
      """
          Pytorch Lightning module for semi-supervised learning.
  
      Args:
          active_dataset (ActiveLearningDataset): active learning dataset
          hparams (Namespace): hyper-parameters for the module
          **kwargs (**dict): extra arguments
      """
  
      def __init__(self, active_dataset: ActiveLearningDataset, **kwargs):
          super().__init__()
          self.save_hyperparameters()
          self.active_dataset = active_dataset
  
      def supervised_training_step(self, batch, *args) -> Dict:
          raise NotImplementedError
  
      def unsupervised_training_step(self, batch, *args) -> Dict:
          raise NotImplementedError
  
      def training_step(self, batch, *args):
          if SemiSupervisedIterator.is_labeled(batch):
              return self.supervised_training_step(SemiSupervisedIterator.get_batch(batch), *args)
          else:
              return self.unsupervised_training_step(SemiSupervisedIterator.get_batch(batch), *args)
  
      def train_dataloader(self) -> SemiSupervisedIterator:
          """SemiSupervisedIterator for train set.
  
          Returns:
              SemiSupervisedIterator on the train set + pool set.
          """
          return SemiSupervisedIterator(
              self.active_dataset,
              self.hparams.batch_size,
              num_steps=self.hparams.num_steps,
              p=self.hparams.p,
              num_workers=self.hparams.workers,
              shuffle=True,
          )
  
      @staticmethod
      def add_model_specific_args(parent_parser):
          """
          Add model specific arguments to argparser.
  
          Args:
              parent_parser (argparse.ArgumentParser): parent parser to which to add arguments
  
          Returns:
              argparser with added arguments
          """
          parser = argparse.ArgumentParser(
              parents=[parent_parser], add_help=True, conflict_handler="resolve"
          )
          parser.add_argument(
              "--p", default=None, type=float, help="Probability of selecting labeled batch"
          )
          parser.add_argument("--num_steps", default=None, type=int, help="Number of steps per epoch")
          parser.add_argument("--batch-size", default=32, type=int)
          parser.add_argument("--workers", default=4, type=int)
  
          return parser
  